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Adaptive Proportional Integral Robust Control of an Uncertain Robotic Manipulator Based on Deep Deterministic Policy Gradient

Author

Listed:
  • Puwei Lu

    (School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Wenkai Huang

    (School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Junlong Xiao

    (School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Fobao Zhou

    (School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Wei Hu

    (School of Mechanical & Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

Abstract

An adaptive proportional integral robust (PIR) control method based on deep deterministic policy gradient (DDPGPIR) is proposed for n-link robotic manipulator systems with model uncertainty and time-varying external disturbances. In this paper, the uncertainty of the nonlinear dynamic model, time-varying external disturbance, and friction resistance of the n-link robotic manipulator are integrated into the uncertainty of the system, and the adaptive robust term is used to compensate for the uncertainty of the system. In addition, dynamic information of the n-link robotic manipulator is used as the input of the DDPG agent to search for the optimal parameters of the proportional integral robust controller in continuous action space. To ensure the DDPG agent’s stable and efficient learning, a reward function combining a Gaussian function and the Euclidean distance is designed. Finally, taking a two-link robot as an example, the simulation experiments of DDPGPIR and other control methods are compared. The results show that DDPGPIR has better adaptive ability, robustness, and higher trajectory tracking accuracy.

Suggested Citation

  • Puwei Lu & Wenkai Huang & Junlong Xiao & Fobao Zhou & Wei Hu, 2021. "Adaptive Proportional Integral Robust Control of an Uncertain Robotic Manipulator Based on Deep Deterministic Policy Gradient," Mathematics, MDPI, vol. 9(17), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2055-:d:622288
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    References listed on IDEAS

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    1. Abhijit Gosavi, 2009. "Reinforcement Learning: A Tutorial Survey and Recent Advances," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 178-192, May.
    2. Jiutai Liu & Xiucheng Dong & Yong Yang & Hongyu Chen, 2021. "Trajectory Tracking Control for Uncertain Robot Manipulators with Repetitive Motions in Task Space," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-17, January.
    3. Sanxiu Wang, 2020. "Adaptive Fuzzy Sliding Mode and Robust Tracking Control for Manipulators with Uncertain Dynamics," Complexity, Hindawi, vol. 2020, pages 1-9, July.
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    Cited by:

    1. Nguyen Xuan-Mung & Mehdi Golestani, 2022. "Smooth, Singularity-Free, Finite-Time Tracking Control for Euler–Lagrange Systems," Mathematics, MDPI, vol. 10(20), pages 1-18, October.

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